In [ ]:
plt.figure(figsize=(9, 9))
for idx, image_data in enumerate(X[:100]):
plt.subplot(10, 10, idx + 1)
plot_image(image_data)
plt.subplots_adjust(wspace=0, hspace=0)
plt.show()
from sklearn.datasets import fetch_openml
from matplotlib import pyplot as plt
import keras
import seaborn as sns;
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense
from tensorflow.keras.utils import to_categorical
import numpy as np
import pandas as pd
import matplotlib.ticker as ticker
data = fetch_openml('Fashion-MNIST', as_frame=False, parser='auto')
list(data)
['data', 'target', 'frame', 'categories', 'feature_names', 'target_names', 'DESCR', 'details', 'url']
print(data.DESCR)
**Author**: Han Xiao, Kashif Rasul, Roland Vollgraf **Source**: [Zalando Research](https://github.com/zalandoresearch/fashion-mnist) **Please cite**: Han Xiao and Kashif Rasul and Roland Vollgraf, Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms, arXiv, cs.LG/1708.07747 Fashion-MNIST is a dataset of Zalando's article images, consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. Fashion-MNIST is intended to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits. Raw data available at: https://github.com/zalandoresearch/fashion-mnist ### Target classes Each training and test example is assigned to one of the following labels: Label Description 0 T-shirt/top 1 Trouser 2 Pullover 3 Dress 4 Coat 5 Sandal 6 Shirt 7 Sneaker 8 Bag 9 Ankle boot Downloaded from openml.org.
data.data.shape
(70000, 784)
data.target.shape
(70000,)
X = data.data
y = to_categorical(data.target)
def plot_image(image_data):
image = image_data.reshape(28, 28)
plt.imshow(image, cmap="binary")
plt.axis("off")
plt.figure(figsize=(9, 9))
for idx, image_data in enumerate(X[:100]):
plt.subplot(10, 10, idx + 1)
plot_image(image_data)
plt.subplots_adjust(wspace=0, hspace=0)
plt.show()
X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]
X_train = X_train.reshape((60000, 28 * 28)) # flattening the 28x28 image into dim=1 (784,) vector (and over all images in the training set)
X_test = X_test.reshape((10000, 28 * 28))
X_train = X_train.astype("float32") / 255 # normalizing the data to the range [0,1] by min-max range, and casting it to float32
X_test = X_test.astype("float32") / 255
def make_model(model_params):
model = Sequential()
if len(model_params['layers']) == 0:
model.add(Dense(model_params['output'], input_dim=model_params['input'], activation='softmax'))
else:
model.add(Dense(model_params['layers'][-1], input_dim=model_params['input'], activation='relu'))
for i in range(len(model_params['layers']) - 1):
model.add(Dense(i, activation='relu'))
model.add(Dense(10, activation='softmax'))
return model
def fit_model(model, epochs, batch_size, verbose):
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, verbose=verbose)
return model, history
def one_hot_to_label(one_hot):
label = list()
for i in range(len(one_hot)):
label.append(np.argmax(one_hot[i]))
return label
def evaluate_model(y_test, y_pred, labels):
y_pred = one_hot_to_label(y_pred)
y_test = one_hot_to_label(y_test)
cm = confusion_matrix(y_test, y_pred)
cr = classification_report(y_test, y_pred)
sns.heatmap(cm.T, square=True, annot=True, fmt='d', xticklabels = labels, yticklabels = labels)
plt.xlabel('true label')
plt.ylabel('predicted label')
print(cr)
plt.show()
return cm, cr
def evaluate_histories(histories, models, figsize):
_,(ax1,ax2)=plt.subplots(2,figsize=figsize)
ax1.xaxis.set_major_locator(ticker.MaxNLocator(integer=True))
ax2.xaxis.set_major_locator(ticker.MaxNLocator(integer=True))
for i in range(len(histories)):
val_acc = histories[i].history['accuracy']
val_loss = histories[i].history['loss']
ax1.plot(val_acc, label=models[i])
ax2.plot(val_loss, label=models[i])
ax1.set_ylabel('Accuracy')
ax2.set_ylabel('Loss')
ax1.set_xlabel('Epochs')
ax2.set_xlabel('Epochs')
ax1.legend()
ax2.legend()
plt.show()
def build_and_evaluate_model(model_params):
model = make_model(model_params)
model, history = fit_model(model,model_params['epochs'], model_params['batch_size'], model_params['verbose'] )
y_pred = model.predict(X_test, verbose=0)
cm, cr = evaluate_model(y_test, y_pred, model_params['labels'])
return history, cm, cr
def classification_report_to_df(cr):
cr_data = [x.split() for x in cr.split('\n')]
cr_df = pd.DataFrame(columns=cr_data[0][:-1])
for data in cr_data[2:12]:
data_df = pd.DataFrame([data[1:-1]], columns=cr_data[0][:-1])
cr_df = pd.concat([cr_df, data_df], ignore_index=True)
return(cr_df)
def evaluate_classification_reports(cr_array):
df = pd.DataFrame()
for i in range(len(cr_array)):
cr_df = classification_report_to_df(cr_array[i])[3:]
cr_df.index = cr_df.index + 1
cr_df = cr_df.stack()
cr_df.index = cr_df.index.map('{0[1]}_{0[0]}'.format)
cr_df = cr_df.to_frame().T
df = pd.concat([df, cr_df], ignore_index=True)
object_columns = df.columns[df.dtypes.eq(object)]
df[object_columns] = df[object_columns].apply(
pd.to_numeric,
errors='coerce',
axis=0
)
df_min = pd.DataFrame(df).T
df_max = pd.DataFrame(df.idxmax()).value_counts()
best_model = df_max.T.idxmax()[0]
stable_model = df_min.min().idxmax()
return best_model, stable_model
def build_and_evaluate_models(models):
params = {
'labels': ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"],
'epochs': 5,
'batch_size': 128,
'input': 784,
'output': 10,
'verbose': 0
}
model_results = {
'models': models,
'history_array':[],
'cm_array':[],
'cr_array':[]
}
for model in models:
print(model)
params['layers'] = model
history, confusion_matrix, classification_report, = build_and_evaluate_model(params)
model_results['history_array'].append(history)
model_results['cm_array'].append(confusion_matrix)
model_results['cr_array'].append(classification_report)
evaluate_histories(model_results['history_array'], models, (8, 100))
return evaluate_classification_reports(model_results['cr_array'])
def make_model_threading(layers, input, output):
model = Sequential()
if len(layers) == 0:
model.add(Dense(output, input_dim=input, activation='softmax'))
else:
model.add(Dense(layers[-1], input_dim=input, activation='relu'))
for i in range(len(layers) - 1):
model.add(Dense(i, activation='relu'))
model.add(Dense(10, activation='softmax'))
return model
def build_and_evaluate_model_threading(epochs, batch_size, input, output, verbose, layers, result, index):
model = make_model_threading(layers, input, output)
model, history = fit_model(model,epochs, batch_size, verbose)
y_pred = model.predict(X_test, verbose=0)
result[index] = {'history': history, 'y_test': y_test, 'y_pred': y_pred, 'labels': y_pred}
from threading import Thread
def build_and_evaluate_models_threading(models):
labels = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"]
epochs = 100
batch_size = 128
input = 784
output = 10
verbose = 0
model_results = {
'models': models,
'history_array':[],
'cm_array':[],
'cr_array':[]
}
threads = [None] * len(models)
results = [None] * len(models)
for i in range(len(threads)):
print(models[i])
threads[i] = Thread(target=build_and_evaluate_model_threading, args=(epochs, batch_size, input, output, verbose, models[i], results, i))
threads[i].start()
for i in range(len(threads)):
threads[i].join()
for i in range(len(results)):
cm, cr = evaluate_model(y_test, results[i]['y_pred'], labels)
model_results['history_array'].append(results[i]['history'])
model_results['cm_array'].append(cm)
model_results['cr_array'].append(cr)
evaluate_histories(model_results['history_array'], models, (8, 100))
return evaluate_classification_reports(model_results['cr_array'])
import logging
import threading
import time
def foo(bar, result, index):
print ('hello {0}'.format(bar))
result[index] = "foo"
from threading import Thread
threads = [None] * 10
results = [None] * 10
for i in range(len(threads)):
threads[i] = Thread(target=foo, args=('world!', results, i))
threads[i].start()
# do some other stuff
for i in range(len(threads)):
threads[i].join()
print(" ".join(results))
hello world! hello world! hello world! hello world! hello world! hello world! hello world! hello world! hello world! hello world! foo foo foo foo foo foo foo foo foo foo
models = np.reshape(np.arange(10, 1000, 10), (-1, 1))
best_model, stable_model = build_and_evaluate_models(models)
print('Best model: ', models[best_model])
print('Stable model: ', models[stable_model])
[10]
precision recall f1-score support
0 0.80 0.81 0.81 1000
1 0.98 0.96 0.97 1000
2 0.79 0.69 0.74 1000
3 0.85 0.85 0.85 1000
4 0.78 0.72 0.75 1000
5 0.94 0.94 0.94 1000
6 0.58 0.71 0.64 1000
7 0.89 0.96 0.93 1000
8 0.95 0.95 0.95 1000
9 0.97 0.89 0.93 1000
accuracy 0.85 10000
macro avg 0.85 0.85 0.85 10000
weighted avg 0.85 0.85 0.85 10000
[20]
precision recall f1-score support
0 0.78 0.87 0.82 1000
1 0.97 0.97 0.97 1000
2 0.78 0.77 0.78 1000
3 0.83 0.89 0.86 1000
4 0.76 0.80 0.78 1000
5 0.95 0.95 0.95 1000
6 0.74 0.58 0.65 1000
7 0.93 0.94 0.94 1000
8 0.97 0.94 0.95 1000
9 0.95 0.94 0.95 1000
accuracy 0.87 10000
macro avg 0.87 0.87 0.86 10000
weighted avg 0.87 0.87 0.86 10000
[30]
precision recall f1-score support
0 0.85 0.77 0.81 1000
1 0.97 0.97 0.97 1000
2 0.80 0.73 0.77 1000
3 0.83 0.90 0.86 1000
4 0.73 0.83 0.78 1000
5 0.96 0.94 0.95 1000
6 0.68 0.64 0.66 1000
7 0.92 0.95 0.94 1000
8 0.95 0.95 0.95 1000
9 0.94 0.95 0.94 1000
accuracy 0.86 10000
macro avg 0.86 0.86 0.86 10000
weighted avg 0.86 0.86 0.86 10000
[40]
precision recall f1-score support
0 0.81 0.85 0.83 1000
1 0.99 0.96 0.97 1000
2 0.73 0.84 0.78 1000
3 0.86 0.89 0.88 1000
4 0.81 0.77 0.79 1000
5 0.96 0.94 0.95 1000
6 0.75 0.65 0.69 1000
7 0.93 0.95 0.94 1000
8 0.97 0.94 0.95 1000
9 0.94 0.95 0.95 1000
accuracy 0.87 10000
macro avg 0.88 0.87 0.87 10000
weighted avg 0.88 0.87 0.87 10000
[50]
precision recall f1-score support
0 0.82 0.82 0.82 1000
1 0.98 0.98 0.98 1000
2 0.75 0.83 0.79 1000
3 0.90 0.87 0.89 1000
4 0.77 0.82 0.79 1000
5 0.97 0.95 0.96 1000
6 0.72 0.62 0.67 1000
7 0.94 0.96 0.95 1000
8 0.97 0.95 0.96 1000
9 0.94 0.95 0.95 1000
accuracy 0.88 10000
macro avg 0.88 0.88 0.87 10000
weighted avg 0.88 0.88 0.87 10000
[60]
precision recall f1-score support
0 0.77 0.88 0.82 1000
1 0.98 0.97 0.97 1000
2 0.75 0.82 0.78 1000
3 0.93 0.82 0.87 1000
4 0.79 0.81 0.80 1000
5 0.97 0.96 0.96 1000
6 0.73 0.64 0.68 1000
7 0.94 0.96 0.95 1000
8 0.97 0.95 0.96 1000
9 0.96 0.95 0.95 1000
accuracy 0.88 10000
macro avg 0.88 0.88 0.88 10000
weighted avg 0.88 0.88 0.88 10000
[70]
precision recall f1-score support
0 0.79 0.84 0.81 1000
1 0.99 0.96 0.98 1000
2 0.78 0.80 0.79 1000
3 0.92 0.82 0.87 1000
4 0.84 0.74 0.79 1000
5 0.98 0.94 0.96 1000
6 0.63 0.76 0.69 1000
7 0.93 0.95 0.94 1000
8 0.97 0.95 0.96 1000
9 0.94 0.96 0.95 1000
accuracy 0.87 10000
macro avg 0.88 0.87 0.87 10000
weighted avg 0.88 0.87 0.87 10000
[80]
precision recall f1-score support
0 0.81 0.83 0.82 1000
1 0.98 0.98 0.98 1000
2 0.83 0.72 0.77 1000
3 0.91 0.87 0.89 1000
4 0.69 0.92 0.79 1000
5 0.98 0.95 0.96 1000
6 0.73 0.62 0.67 1000
7 0.94 0.96 0.95 1000
8 0.97 0.96 0.96 1000
9 0.95 0.96 0.95 1000
accuracy 0.88 10000
macro avg 0.88 0.88 0.88 10000
weighted avg 0.88 0.88 0.88 10000
[90]
precision recall f1-score support
0 0.87 0.78 0.82 1000
1 0.98 0.97 0.97 1000
2 0.76 0.84 0.80 1000
3 0.86 0.88 0.87 1000
4 0.80 0.80 0.80 1000
5 0.96 0.95 0.95 1000
6 0.70 0.69 0.70 1000
7 0.92 0.96 0.94 1000
8 0.97 0.95 0.96 1000
9 0.96 0.94 0.95 1000
accuracy 0.88 10000
macro avg 0.88 0.88 0.88 10000
weighted avg 0.88 0.88 0.88 10000
[100]
precision recall f1-score support
0 0.80 0.85 0.82 1000
1 0.99 0.97 0.98 1000
2 0.79 0.84 0.81 1000
3 0.91 0.87 0.89 1000
4 0.84 0.79 0.82 1000
5 0.97 0.96 0.97 1000
6 0.71 0.71 0.71 1000
7 0.95 0.95 0.95 1000
8 0.97 0.96 0.97 1000
9 0.95 0.97 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[110]
precision recall f1-score support
0 0.82 0.81 0.82 1000
1 0.99 0.98 0.98 1000
2 0.80 0.81 0.80 1000
3 0.88 0.90 0.89 1000
4 0.84 0.80 0.82 1000
5 0.98 0.96 0.97 1000
6 0.68 0.71 0.70 1000
7 0.94 0.96 0.95 1000
8 0.97 0.97 0.97 1000
9 0.95 0.95 0.95 1000
accuracy 0.88 10000
macro avg 0.89 0.88 0.88 10000
weighted avg 0.89 0.88 0.88 10000
[120]
precision recall f1-score support
0 0.85 0.80 0.83 1000
1 0.99 0.97 0.98 1000
2 0.83 0.74 0.78 1000
3 0.88 0.89 0.89 1000
4 0.75 0.86 0.80 1000
5 0.97 0.96 0.97 1000
6 0.70 0.70 0.70 1000
7 0.95 0.95 0.95 1000
8 0.96 0.97 0.96 1000
9 0.95 0.96 0.96 1000
accuracy 0.88 10000
macro avg 0.88 0.88 0.88 10000
weighted avg 0.88 0.88 0.88 10000
[130]
precision recall f1-score support
0 0.85 0.80 0.82 1000
1 0.99 0.98 0.98 1000
2 0.86 0.67 0.76 1000
3 0.90 0.89 0.90 1000
4 0.76 0.87 0.81 1000
5 0.97 0.97 0.97 1000
6 0.63 0.74 0.68 1000
7 0.94 0.97 0.96 1000
8 0.98 0.96 0.97 1000
9 0.97 0.95 0.96 1000
accuracy 0.88 10000
macro avg 0.89 0.88 0.88 10000
weighted avg 0.89 0.88 0.88 10000
[140]
precision recall f1-score support
0 0.84 0.85 0.84 1000
1 0.99 0.98 0.98 1000
2 0.80 0.79 0.80 1000
3 0.85 0.92 0.88 1000
4 0.81 0.80 0.80 1000
5 0.98 0.96 0.97 1000
6 0.75 0.69 0.72 1000
7 0.95 0.96 0.96 1000
8 0.96 0.97 0.97 1000
9 0.96 0.96 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[150]
precision recall f1-score support
0 0.83 0.85 0.84 1000
1 0.99 0.97 0.98 1000
2 0.82 0.80 0.81 1000
3 0.91 0.88 0.89 1000
4 0.80 0.84 0.82 1000
5 0.97 0.96 0.96 1000
6 0.72 0.72 0.72 1000
7 0.94 0.95 0.95 1000
8 0.97 0.96 0.97 1000
9 0.95 0.96 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[160]
precision recall f1-score support
0 0.82 0.86 0.84 1000
1 0.99 0.97 0.98 1000
2 0.82 0.80 0.81 1000
3 0.91 0.89 0.90 1000
4 0.82 0.82 0.82 1000
5 0.98 0.96 0.97 1000
6 0.70 0.70 0.70 1000
7 0.94 0.97 0.95 1000
8 0.97 0.97 0.97 1000
9 0.96 0.95 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[170]
precision recall f1-score support
0 0.86 0.81 0.83 1000
1 0.98 0.98 0.98 1000
2 0.80 0.81 0.80 1000
3 0.90 0.89 0.89 1000
4 0.78 0.86 0.82 1000
5 0.98 0.96 0.97 1000
6 0.73 0.71 0.72 1000
7 0.95 0.96 0.96 1000
8 0.99 0.96 0.97 1000
9 0.95 0.97 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[180]
precision recall f1-score support
0 0.81 0.86 0.84 1000
1 0.99 0.98 0.99 1000
2 0.77 0.86 0.81 1000
3 0.87 0.92 0.89 1000
4 0.80 0.84 0.82 1000
5 0.97 0.96 0.96 1000
6 0.82 0.59 0.69 1000
7 0.95 0.94 0.94 1000
8 0.98 0.96 0.97 1000
9 0.93 0.97 0.95 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[190]
precision recall f1-score support
0 0.85 0.81 0.83 1000
1 0.99 0.98 0.98 1000
2 0.77 0.87 0.82 1000
3 0.87 0.91 0.89 1000
4 0.81 0.82 0.81 1000
5 0.97 0.97 0.97 1000
6 0.75 0.65 0.70 1000
7 0.95 0.96 0.95 1000
8 0.97 0.96 0.97 1000
9 0.96 0.96 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[200]
precision recall f1-score support
0 0.81 0.86 0.84 1000
1 0.99 0.97 0.98 1000
2 0.82 0.80 0.81 1000
3 0.88 0.90 0.89 1000
4 0.76 0.87 0.81 1000
5 0.97 0.96 0.97 1000
6 0.79 0.62 0.69 1000
7 0.94 0.97 0.95 1000
8 0.97 0.96 0.97 1000
9 0.96 0.95 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[210]
precision recall f1-score support
0 0.82 0.86 0.84 1000
1 0.98 0.98 0.98 1000
2 0.75 0.87 0.81 1000
3 0.91 0.88 0.89 1000
4 0.89 0.72 0.80 1000
5 0.97 0.96 0.97 1000
6 0.71 0.72 0.72 1000
7 0.93 0.97 0.95 1000
8 0.97 0.96 0.97 1000
9 0.96 0.94 0.95 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[220]
precision recall f1-score support
0 0.84 0.85 0.84 1000
1 0.99 0.98 0.98 1000
2 0.80 0.80 0.80 1000
3 0.90 0.90 0.90 1000
4 0.78 0.84 0.81 1000
5 0.97 0.96 0.96 1000
6 0.75 0.68 0.71 1000
7 0.94 0.95 0.95 1000
8 0.96 0.97 0.97 1000
9 0.94 0.96 0.95 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[230]
precision recall f1-score support
0 0.82 0.87 0.84 1000
1 0.99 0.98 0.98 1000
2 0.82 0.79 0.81 1000
3 0.89 0.90 0.90 1000
4 0.82 0.83 0.82 1000
5 0.98 0.96 0.97 1000
6 0.73 0.70 0.71 1000
7 0.93 0.98 0.95 1000
8 0.97 0.97 0.97 1000
9 0.97 0.95 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[240]
precision recall f1-score support
0 0.84 0.83 0.84 1000
1 0.99 0.98 0.99 1000
2 0.88 0.69 0.78 1000
3 0.92 0.87 0.89 1000
4 0.75 0.89 0.81 1000
5 0.97 0.96 0.97 1000
6 0.69 0.75 0.72 1000
7 0.94 0.96 0.95 1000
8 0.97 0.96 0.97 1000
9 0.96 0.96 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[250]
precision recall f1-score support
0 0.82 0.87 0.85 1000
1 0.99 0.97 0.98 1000
2 0.72 0.91 0.81 1000
3 0.92 0.88 0.90 1000
4 0.82 0.80 0.81 1000
5 0.97 0.96 0.97 1000
6 0.82 0.60 0.69 1000
7 0.95 0.96 0.95 1000
8 0.96 0.97 0.97 1000
9 0.95 0.96 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[260]
precision recall f1-score support
0 0.83 0.85 0.84 1000
1 0.98 0.97 0.98 1000
2 0.77 0.86 0.81 1000
3 0.87 0.92 0.89 1000
4 0.84 0.79 0.81 1000
5 0.97 0.96 0.97 1000
6 0.78 0.67 0.72 1000
7 0.94 0.96 0.95 1000
8 0.97 0.97 0.97 1000
9 0.95 0.95 0.95 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[270]
precision recall f1-score support
0 0.84 0.85 0.85 1000
1 0.99 0.98 0.98 1000
2 0.77 0.83 0.80 1000
3 0.89 0.91 0.90 1000
4 0.81 0.83 0.82 1000
5 0.98 0.96 0.97 1000
6 0.78 0.69 0.73 1000
7 0.93 0.97 0.95 1000
8 0.97 0.96 0.96 1000
9 0.96 0.95 0.95 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[280]
precision recall f1-score support
0 0.88 0.79 0.83 1000
1 0.99 0.98 0.98 1000
2 0.74 0.87 0.80 1000
3 0.87 0.91 0.89 1000
4 0.84 0.76 0.80 1000
5 0.97 0.96 0.97 1000
6 0.73 0.71 0.72 1000
7 0.93 0.97 0.95 1000
8 0.97 0.97 0.97 1000
9 0.97 0.95 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[290]
precision recall f1-score support
0 0.80 0.87 0.83 1000
1 0.99 0.98 0.98 1000
2 0.82 0.81 0.82 1000
3 0.88 0.90 0.89 1000
4 0.85 0.80 0.82 1000
5 0.98 0.96 0.97 1000
6 0.73 0.71 0.72 1000
7 0.92 0.98 0.95 1000
8 0.98 0.95 0.97 1000
9 0.97 0.94 0.95 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[300]
precision recall f1-score support
0 0.80 0.86 0.83 1000
1 0.99 0.98 0.98 1000
2 0.86 0.72 0.79 1000
3 0.93 0.85 0.89 1000
4 0.73 0.90 0.81 1000
5 0.97 0.97 0.97 1000
6 0.74 0.69 0.71 1000
7 0.95 0.97 0.96 1000
8 0.98 0.97 0.97 1000
9 0.96 0.96 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[310]
precision recall f1-score support
0 0.85 0.85 0.85 1000
1 0.98 0.98 0.98 1000
2 0.86 0.69 0.77 1000
3 0.92 0.88 0.90 1000
4 0.73 0.86 0.79 1000
5 0.98 0.97 0.97 1000
6 0.69 0.74 0.72 1000
7 0.96 0.95 0.96 1000
8 0.97 0.97 0.97 1000
9 0.95 0.97 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[320]
precision recall f1-score support
0 0.82 0.87 0.84 1000
1 0.99 0.97 0.98 1000
2 0.82 0.80 0.81 1000
3 0.88 0.93 0.90 1000
4 0.81 0.84 0.83 1000
5 0.98 0.96 0.97 1000
6 0.75 0.68 0.71 1000
7 0.94 0.97 0.96 1000
8 0.98 0.97 0.98 1000
9 0.96 0.96 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[330]
precision recall f1-score support
0 0.87 0.79 0.83 1000
1 0.98 0.98 0.98 1000
2 0.79 0.83 0.81 1000
3 0.92 0.87 0.90 1000
4 0.80 0.84 0.82 1000
5 0.98 0.96 0.97 1000
6 0.71 0.73 0.72 1000
7 0.95 0.96 0.96 1000
8 0.97 0.97 0.97 1000
9 0.95 0.96 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[340]
precision recall f1-score support
0 0.81 0.86 0.83 1000
1 0.99 0.98 0.98 1000
2 0.83 0.79 0.81 1000
3 0.88 0.92 0.90 1000
4 0.79 0.85 0.82 1000
5 0.98 0.97 0.97 1000
6 0.78 0.66 0.71 1000
7 0.95 0.96 0.96 1000
8 0.97 0.98 0.98 1000
9 0.95 0.97 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[350]
precision recall f1-score support
0 0.85 0.81 0.83 1000
1 0.99 0.97 0.98 1000
2 0.81 0.81 0.81 1000
3 0.90 0.89 0.90 1000
4 0.79 0.85 0.82 1000
5 0.97 0.97 0.97 1000
6 0.71 0.72 0.71 1000
7 0.95 0.97 0.96 1000
8 0.99 0.95 0.97 1000
9 0.96 0.96 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[360]
precision recall f1-score support
0 0.80 0.90 0.84 1000
1 0.99 0.98 0.98 1000
2 0.79 0.85 0.82 1000
3 0.90 0.89 0.90 1000
4 0.84 0.81 0.82 1000
5 0.98 0.97 0.97 1000
6 0.78 0.67 0.72 1000
7 0.96 0.95 0.96 1000
8 0.98 0.97 0.97 1000
9 0.95 0.97 0.96 1000
accuracy 0.90 10000
macro avg 0.90 0.90 0.89 10000
weighted avg 0.90 0.90 0.89 10000
[370]
precision recall f1-score support
0 0.88 0.76 0.81 1000
1 0.98 0.98 0.98 1000
2 0.82 0.74 0.78 1000
3 0.92 0.89 0.90 1000
4 0.83 0.80 0.81 1000
5 0.98 0.97 0.97 1000
6 0.62 0.81 0.70 1000
7 0.95 0.97 0.96 1000
8 0.98 0.96 0.97 1000
9 0.97 0.96 0.97 1000
accuracy 0.88 10000
macro avg 0.89 0.88 0.89 10000
weighted avg 0.89 0.88 0.89 10000
[380]
precision recall f1-score support
0 0.87 0.79 0.83 1000
1 0.99 0.98 0.99 1000
2 0.85 0.74 0.79 1000
3 0.89 0.90 0.90 1000
4 0.83 0.80 0.82 1000
5 0.98 0.97 0.97 1000
6 0.65 0.81 0.72 1000
7 0.95 0.97 0.96 1000
8 0.98 0.97 0.97 1000
9 0.96 0.96 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[390]
precision recall f1-score support
0 0.87 0.84 0.85 1000
1 0.99 0.98 0.98 1000
2 0.84 0.78 0.81 1000
3 0.92 0.86 0.89 1000
4 0.77 0.88 0.82 1000
5 0.97 0.97 0.97 1000
6 0.72 0.75 0.73 1000
7 0.95 0.96 0.96 1000
8 0.98 0.97 0.98 1000
9 0.96 0.96 0.96 1000
accuracy 0.89 10000
macro avg 0.90 0.89 0.90 10000
weighted avg 0.90 0.89 0.90 10000
[400]
precision recall f1-score support
0 0.86 0.84 0.85 1000
1 0.98 0.98 0.98 1000
2 0.76 0.85 0.81 1000
3 0.93 0.84 0.88 1000
4 0.74 0.86 0.80 1000
5 0.98 0.96 0.97 1000
6 0.78 0.63 0.70 1000
7 0.94 0.96 0.95 1000
8 0.96 0.98 0.97 1000
9 0.95 0.96 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[410]
precision recall f1-score support
0 0.82 0.85 0.83 1000
1 0.99 0.98 0.99 1000
2 0.82 0.81 0.81 1000
3 0.91 0.90 0.90 1000
4 0.83 0.80 0.82 1000
5 0.98 0.97 0.97 1000
6 0.70 0.73 0.71 1000
7 0.95 0.97 0.96 1000
8 0.98 0.97 0.98 1000
9 0.96 0.96 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[420]
precision recall f1-score support
0 0.85 0.82 0.84 1000
1 0.99 0.99 0.99 1000
2 0.79 0.82 0.80 1000
3 0.90 0.91 0.90 1000
4 0.79 0.83 0.81 1000
5 0.98 0.97 0.97 1000
6 0.74 0.69 0.71 1000
7 0.95 0.97 0.96 1000
8 0.98 0.97 0.98 1000
9 0.97 0.96 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[430]
precision recall f1-score support
0 0.86 0.81 0.84 1000
1 0.99 0.97 0.98 1000
2 0.85 0.71 0.77 1000
3 0.86 0.93 0.89 1000
4 0.85 0.75 0.80 1000
5 0.97 0.96 0.97 1000
6 0.63 0.80 0.70 1000
7 0.95 0.96 0.95 1000
8 0.98 0.97 0.97 1000
9 0.96 0.96 0.96 1000
accuracy 0.88 10000
macro avg 0.89 0.88 0.88 10000
weighted avg 0.89 0.88 0.88 10000
[440]
precision recall f1-score support
0 0.80 0.88 0.84 1000
1 0.99 0.98 0.99 1000
2 0.77 0.86 0.82 1000
3 0.92 0.90 0.91 1000
4 0.81 0.83 0.82 1000
5 0.98 0.97 0.97 1000
6 0.81 0.63 0.71 1000
7 0.93 0.98 0.95 1000
8 0.98 0.97 0.97 1000
9 0.97 0.95 0.96 1000
accuracy 0.89 10000
macro avg 0.90 0.89 0.89 10000
weighted avg 0.90 0.89 0.89 10000
[450]
precision recall f1-score support
0 0.83 0.87 0.85 1000
1 0.99 0.98 0.98 1000
2 0.84 0.79 0.81 1000
3 0.90 0.90 0.90 1000
4 0.80 0.86 0.83 1000
5 0.98 0.97 0.97 1000
6 0.77 0.71 0.74 1000
7 0.95 0.96 0.96 1000
8 0.97 0.97 0.97 1000
9 0.96 0.97 0.96 1000
accuracy 0.90 10000
macro avg 0.90 0.90 0.90 10000
weighted avg 0.90 0.90 0.90 10000
[460]
precision recall f1-score support
0 0.87 0.81 0.84 1000
1 0.98 0.98 0.98 1000
2 0.89 0.68 0.77 1000
3 0.92 0.88 0.90 1000
4 0.70 0.92 0.79 1000
5 0.97 0.97 0.97 1000
6 0.73 0.74 0.73 1000
7 0.96 0.94 0.95 1000
8 0.97 0.97 0.97 1000
9 0.93 0.97 0.95 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[470]
precision recall f1-score support
0 0.78 0.89 0.83 1000
1 0.99 0.98 0.99 1000
2 0.77 0.88 0.82 1000
3 0.92 0.89 0.90 1000
4 0.84 0.79 0.82 1000
5 0.97 0.98 0.97 1000
6 0.78 0.64 0.70 1000
7 0.95 0.96 0.96 1000
8 0.97 0.97 0.97 1000
9 0.97 0.96 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[480]
precision recall f1-score support
0 0.83 0.86 0.84 1000
1 0.99 0.98 0.99 1000
2 0.80 0.84 0.82 1000
3 0.89 0.91 0.90 1000
4 0.84 0.82 0.83 1000
5 0.98 0.97 0.97 1000
6 0.76 0.69 0.72 1000
7 0.95 0.96 0.96 1000
8 0.97 0.97 0.97 1000
9 0.95 0.97 0.96 1000
accuracy 0.90 10000
macro avg 0.90 0.90 0.90 10000
weighted avg 0.90 0.90 0.90 10000
[490]
precision recall f1-score support
0 0.87 0.80 0.83 1000
1 0.99 0.98 0.98 1000
2 0.79 0.84 0.82 1000
3 0.89 0.90 0.89 1000
4 0.80 0.85 0.82 1000
5 0.98 0.97 0.97 1000
6 0.74 0.72 0.73 1000
7 0.96 0.96 0.96 1000
8 0.97 0.97 0.97 1000
9 0.96 0.97 0.96 1000
accuracy 0.89 10000
macro avg 0.90 0.89 0.89 10000
weighted avg 0.90 0.89 0.89 10000
[500]
precision recall f1-score support
0 0.83 0.85 0.84 1000
1 0.99 0.98 0.99 1000
2 0.80 0.85 0.83 1000
3 0.93 0.87 0.90 1000
4 0.80 0.85 0.82 1000
5 0.97 0.97 0.97 1000
6 0.75 0.69 0.72 1000
7 0.96 0.95 0.95 1000
8 0.98 0.97 0.97 1000
9 0.96 0.97 0.96 1000
accuracy 0.90 10000
macro avg 0.90 0.90 0.89 10000
weighted avg 0.90 0.90 0.89 10000
[510]
precision recall f1-score support
0 0.81 0.86 0.83 1000
1 0.99 0.98 0.99 1000
2 0.75 0.88 0.81 1000
3 0.87 0.94 0.90 1000
4 0.84 0.77 0.80 1000
5 0.98 0.97 0.97 1000
6 0.78 0.60 0.68 1000
7 0.95 0.97 0.96 1000
8 0.97 0.98 0.98 1000
9 0.97 0.96 0.97 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[520]
precision recall f1-score support
0 0.82 0.86 0.84 1000
1 0.99 0.98 0.99 1000
2 0.82 0.79 0.80 1000
3 0.92 0.89 0.90 1000
4 0.78 0.87 0.82 1000
5 0.98 0.97 0.97 1000
6 0.76 0.70 0.73 1000
7 0.94 0.96 0.95 1000
8 0.97 0.97 0.97 1000
9 0.97 0.95 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[530]
precision recall f1-score support
0 0.84 0.84 0.84 1000
1 0.99 0.98 0.99 1000
2 0.85 0.79 0.82 1000
3 0.89 0.92 0.90 1000
4 0.80 0.84 0.82 1000
5 0.97 0.97 0.97 1000
6 0.74 0.72 0.73 1000
7 0.95 0.96 0.95 1000
8 0.98 0.97 0.97 1000
9 0.97 0.96 0.96 1000
accuracy 0.90 10000
macro avg 0.90 0.90 0.90 10000
weighted avg 0.90 0.90 0.90 10000
[540]
precision recall f1-score support
0 0.86 0.73 0.79 1000
1 0.98 0.98 0.98 1000
2 0.85 0.74 0.79 1000
3 0.90 0.89 0.89 1000
4 0.75 0.87 0.81 1000
5 0.98 0.95 0.97 1000
6 0.66 0.74 0.70 1000
7 0.93 0.97 0.95 1000
8 0.98 0.97 0.97 1000
9 0.96 0.96 0.96 1000
accuracy 0.88 10000
macro avg 0.89 0.88 0.88 10000
weighted avg 0.89 0.88 0.88 10000
[550]
precision recall f1-score support
0 0.87 0.81 0.84 1000
1 0.98 0.99 0.99 1000
2 0.77 0.86 0.81 1000
3 0.91 0.89 0.90 1000
4 0.81 0.82 0.82 1000
5 0.99 0.97 0.98 1000
6 0.73 0.70 0.71 1000
7 0.95 0.97 0.96 1000
8 0.97 0.97 0.97 1000
9 0.96 0.96 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[560]
precision recall f1-score support
0 0.80 0.89 0.84 1000
1 0.99 0.98 0.99 1000
2 0.80 0.82 0.81 1000
3 0.91 0.91 0.91 1000
4 0.81 0.85 0.83 1000
5 0.98 0.97 0.97 1000
6 0.80 0.65 0.72 1000
7 0.95 0.96 0.96 1000
8 0.98 0.97 0.97 1000
9 0.95 0.96 0.96 1000
accuracy 0.90 10000
macro avg 0.90 0.90 0.90 10000
weighted avg 0.90 0.90 0.90 10000
[570]
precision recall f1-score support
0 0.83 0.84 0.83 1000
1 0.99 0.98 0.98 1000
2 0.80 0.85 0.82 1000
3 0.94 0.86 0.90 1000
4 0.81 0.84 0.83 1000
5 0.98 0.96 0.97 1000
6 0.74 0.73 0.74 1000
7 0.94 0.97 0.96 1000
8 0.98 0.97 0.98 1000
9 0.95 0.96 0.96 1000
accuracy 0.90 10000
macro avg 0.90 0.90 0.90 10000
weighted avg 0.90 0.90 0.90 10000
[580]
precision recall f1-score support
0 0.87 0.82 0.84 1000
1 0.99 0.98 0.99 1000
2 0.75 0.87 0.81 1000
3 0.88 0.92 0.90 1000
4 0.84 0.77 0.80 1000
5 0.97 0.97 0.97 1000
6 0.74 0.70 0.72 1000
7 0.96 0.96 0.96 1000
8 0.98 0.97 0.98 1000
9 0.96 0.97 0.97 1000
accuracy 0.89 10000
macro avg 0.90 0.89 0.89 10000
weighted avg 0.90 0.89 0.89 10000
[590]
precision recall f1-score support
0 0.86 0.84 0.85 1000
1 0.99 0.98 0.99 1000
2 0.79 0.84 0.82 1000
3 0.90 0.91 0.90 1000
4 0.79 0.86 0.82 1000
5 0.98 0.97 0.98 1000
6 0.77 0.66 0.71 1000
7 0.96 0.96 0.96 1000
8 0.97 0.97 0.97 1000
9 0.96 0.96 0.96 1000
accuracy 0.90 10000
macro avg 0.90 0.90 0.90 10000
weighted avg 0.90 0.90 0.90 10000
[600]
precision recall f1-score support
0 0.81 0.86 0.84 1000
1 0.99 0.98 0.99 1000
2 0.87 0.76 0.81 1000
3 0.90 0.90 0.90 1000
4 0.79 0.88 0.83 1000
5 0.97 0.98 0.97 1000
6 0.74 0.69 0.72 1000
7 0.95 0.95 0.95 1000
8 0.97 0.98 0.97 1000
9 0.96 0.96 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[610]
precision recall f1-score support
0 0.84 0.84 0.84 1000
1 0.99 0.98 0.99 1000
2 0.83 0.78 0.80 1000
3 0.88 0.92 0.90 1000
4 0.75 0.86 0.80 1000
5 0.98 0.97 0.97 1000
6 0.76 0.67 0.71 1000
7 0.96 0.96 0.96 1000
8 0.98 0.97 0.98 1000
9 0.95 0.97 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[620]
precision recall f1-score support
0 0.82 0.85 0.84 1000
1 0.99 0.98 0.99 1000
2 0.83 0.78 0.81 1000
3 0.86 0.93 0.89 1000
4 0.85 0.79 0.82 1000
5 0.98 0.97 0.98 1000
6 0.71 0.72 0.71 1000
7 0.95 0.96 0.96 1000
8 0.96 0.98 0.97 1000
9 0.96 0.96 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[630]
precision recall f1-score support
0 0.79 0.88 0.83 1000
1 0.99 0.98 0.99 1000
2 0.81 0.84 0.82 1000
3 0.93 0.87 0.90 1000
4 0.83 0.82 0.82 1000
5 0.98 0.97 0.97 1000
6 0.74 0.71 0.72 1000
7 0.96 0.96 0.96 1000
8 0.98 0.97 0.97 1000
9 0.96 0.97 0.96 1000
accuracy 0.90 10000
macro avg 0.90 0.90 0.90 10000
weighted avg 0.90 0.90 0.90 10000
[640]
precision recall f1-score support
0 0.90 0.71 0.79 1000
1 0.99 0.98 0.99 1000
2 0.81 0.84 0.82 1000
3 0.91 0.89 0.90 1000
4 0.86 0.80 0.83 1000
5 0.97 0.98 0.97 1000
6 0.63 0.82 0.71 1000
7 0.96 0.96 0.96 1000
8 0.98 0.96 0.97 1000
9 0.97 0.97 0.97 1000
accuracy 0.89 10000
macro avg 0.90 0.89 0.89 10000
weighted avg 0.90 0.89 0.89 10000
[650]
precision recall f1-score support
0 0.81 0.89 0.85 1000
1 0.98 0.98 0.98 1000
2 0.76 0.84 0.80 1000
3 0.90 0.89 0.90 1000
4 0.85 0.78 0.81 1000
5 0.98 0.98 0.98 1000
6 0.77 0.66 0.71 1000
7 0.95 0.97 0.96 1000
8 0.96 0.98 0.97 1000
9 0.97 0.96 0.97 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[660]
precision recall f1-score support
0 0.82 0.88 0.85 1000
1 0.99 0.99 0.99 1000
2 0.79 0.82 0.80 1000
3 0.93 0.86 0.89 1000
4 0.75 0.87 0.81 1000
5 0.97 0.97 0.97 1000
6 0.79 0.64 0.70 1000
7 0.95 0.96 0.96 1000
8 0.98 0.97 0.98 1000
9 0.96 0.96 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[670]
precision recall f1-score support
0 0.86 0.80 0.83 1000
1 0.98 0.99 0.98 1000
2 0.82 0.82 0.82 1000
3 0.91 0.90 0.91 1000
4 0.83 0.83 0.83 1000
5 0.98 0.97 0.97 1000
6 0.69 0.74 0.72 1000
7 0.95 0.97 0.96 1000
8 0.99 0.97 0.98 1000
9 0.97 0.96 0.96 1000
accuracy 0.90 10000
macro avg 0.90 0.90 0.90 10000
weighted avg 0.90 0.90 0.90 10000
[680]
precision recall f1-score support
0 0.85 0.81 0.83 1000
1 0.99 0.98 0.99 1000
2 0.76 0.86 0.81 1000
3 0.91 0.89 0.90 1000
4 0.82 0.78 0.80 1000
5 0.98 0.97 0.97 1000
6 0.72 0.71 0.71 1000
7 0.95 0.97 0.96 1000
8 0.97 0.97 0.97 1000
9 0.96 0.97 0.97 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[690]
precision recall f1-score support
0 0.85 0.85 0.85 1000
1 0.99 0.99 0.99 1000
2 0.82 0.83 0.82 1000
3 0.89 0.91 0.90 1000
4 0.81 0.85 0.83 1000
5 0.98 0.98 0.98 1000
6 0.75 0.69 0.72 1000
7 0.96 0.94 0.95 1000
8 0.97 0.97 0.97 1000
9 0.95 0.97 0.96 1000
accuracy 0.90 10000
macro avg 0.90 0.90 0.90 10000
weighted avg 0.90 0.90 0.90 10000
[700]
precision recall f1-score support
0 0.88 0.82 0.85 1000
1 0.97 0.99 0.98 1000
2 0.87 0.75 0.81 1000
3 0.92 0.89 0.90 1000
4 0.79 0.84 0.82 1000
5 0.98 0.96 0.97 1000
6 0.69 0.79 0.73 1000
7 0.94 0.97 0.95 1000
8 0.97 0.98 0.98 1000
9 0.96 0.95 0.96 1000
accuracy 0.89 10000
macro avg 0.90 0.89 0.90 10000
weighted avg 0.90 0.89 0.90 10000
[710]
precision recall f1-score support
0 0.82 0.88 0.85 1000
1 0.99 0.98 0.99 1000
2 0.87 0.75 0.81 1000
3 0.91 0.91 0.91 1000
4 0.77 0.89 0.82 1000
5 0.99 0.96 0.97 1000
6 0.77 0.69 0.73 1000
7 0.94 0.98 0.96 1000
8 0.98 0.98 0.98 1000
9 0.96 0.96 0.96 1000
accuracy 0.90 10000
macro avg 0.90 0.90 0.90 10000
weighted avg 0.90 0.90 0.90 10000
[720]
precision recall f1-score support
0 0.83 0.84 0.84 1000
1 0.99 0.98 0.98 1000
2 0.84 0.75 0.79 1000
3 0.91 0.89 0.90 1000
4 0.80 0.85 0.82 1000
5 0.98 0.97 0.97 1000
6 0.70 0.75 0.72 1000
7 0.96 0.96 0.96 1000
8 0.98 0.97 0.98 1000
9 0.96 0.97 0.97 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[730]
precision recall f1-score support
0 0.90 0.79 0.84 1000
1 0.98 0.99 0.98 1000
2 0.81 0.83 0.82 1000
3 0.89 0.92 0.90 1000
4 0.82 0.83 0.82 1000
5 0.98 0.96 0.97 1000
6 0.72 0.75 0.74 1000
7 0.95 0.97 0.96 1000
8 0.97 0.97 0.97 1000
9 0.96 0.97 0.97 1000
accuracy 0.90 10000
macro avg 0.90 0.90 0.90 10000
weighted avg 0.90 0.90 0.90 10000
[740]
precision recall f1-score support
0 0.83 0.84 0.83 1000
1 0.99 0.99 0.99 1000
2 0.79 0.86 0.82 1000
3 0.94 0.86 0.90 1000
4 0.82 0.82 0.82 1000
5 0.97 0.98 0.97 1000
6 0.73 0.71 0.72 1000
7 0.95 0.96 0.96 1000
8 0.97 0.98 0.97 1000
9 0.97 0.96 0.97 1000
accuracy 0.90 10000
macro avg 0.90 0.90 0.90 10000
weighted avg 0.90 0.90 0.90 10000
[750]
precision recall f1-score support
0 0.82 0.88 0.84 1000
1 0.99 0.98 0.99 1000
2 0.78 0.84 0.81 1000
3 0.91 0.90 0.91 1000
4 0.81 0.84 0.83 1000
5 0.98 0.97 0.98 1000
6 0.80 0.66 0.72 1000
7 0.94 0.97 0.95 1000
8 0.98 0.97 0.97 1000
9 0.97 0.95 0.96 1000
accuracy 0.90 10000
macro avg 0.90 0.90 0.90 10000
weighted avg 0.90 0.90 0.90 10000
[760]
precision recall f1-score support
0 0.84 0.83 0.84 1000
1 0.98 0.98 0.98 1000
2 0.86 0.72 0.79 1000
3 0.86 0.94 0.90 1000
4 0.83 0.80 0.81 1000
5 0.97 0.97 0.97 1000
6 0.68 0.76 0.72 1000
7 0.94 0.96 0.95 1000
8 0.97 0.98 0.97 1000
9 0.97 0.95 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[770]
precision recall f1-score support
0 0.87 0.86 0.86 1000
1 0.99 0.98 0.99 1000
2 0.81 0.81 0.81 1000
3 0.95 0.84 0.90 1000
4 0.77 0.87 0.82 1000
5 0.98 0.97 0.98 1000
6 0.74 0.74 0.74 1000
7 0.95 0.97 0.96 1000
8 0.98 0.96 0.97 1000
9 0.97 0.96 0.97 1000
accuracy 0.90 10000
macro avg 0.90 0.90 0.90 10000
weighted avg 0.90 0.90 0.90 10000
[780]
precision recall f1-score support
0 0.85 0.80 0.83 1000
1 0.99 0.97 0.98 1000
2 0.80 0.81 0.80 1000
3 0.91 0.88 0.90 1000
4 0.77 0.84 0.81 1000
5 0.97 0.98 0.97 1000
6 0.72 0.72 0.72 1000
7 0.96 0.94 0.95 1000
8 0.98 0.97 0.98 1000
9 0.95 0.97 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[790]
precision recall f1-score support
0 0.85 0.81 0.83 1000
1 0.99 0.98 0.98 1000
2 0.76 0.87 0.81 1000
3 0.90 0.90 0.90 1000
4 0.86 0.79 0.82 1000
5 0.98 0.97 0.98 1000
6 0.72 0.72 0.72 1000
7 0.96 0.97 0.96 1000
8 0.99 0.97 0.98 1000
9 0.96 0.96 0.96 1000
accuracy 0.89 10000
macro avg 0.90 0.89 0.89 10000
weighted avg 0.90 0.89 0.89 10000
[800]
precision recall f1-score support
0 0.85 0.84 0.84 1000
1 0.99 0.98 0.98 1000
2 0.83 0.81 0.82 1000
3 0.87 0.93 0.90 1000
4 0.85 0.80 0.82 1000
5 0.98 0.96 0.97 1000
6 0.71 0.75 0.73 1000
7 0.91 0.98 0.95 1000
8 0.98 0.97 0.97 1000
9 0.98 0.93 0.95 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[810]
precision recall f1-score support
0 0.84 0.82 0.83 1000
1 0.98 0.99 0.98 1000
2 0.86 0.76 0.81 1000
3 0.93 0.85 0.89 1000
4 0.81 0.83 0.82 1000
5 0.98 0.97 0.98 1000
6 0.67 0.77 0.72 1000
7 0.95 0.96 0.96 1000
8 0.97 0.98 0.97 1000
9 0.96 0.97 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[820]
precision recall f1-score support
0 0.86 0.83 0.84 1000
1 0.98 0.98 0.98 1000
2 0.84 0.76 0.80 1000
3 0.92 0.89 0.91 1000
4 0.76 0.89 0.81 1000
5 0.98 0.96 0.97 1000
6 0.72 0.72 0.72 1000
7 0.95 0.97 0.96 1000
8 0.98 0.97 0.97 1000
9 0.96 0.95 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[830]
precision recall f1-score support
0 0.87 0.77 0.82 1000
1 0.99 0.98 0.99 1000
2 0.77 0.86 0.81 1000
3 0.90 0.91 0.91 1000
4 0.87 0.72 0.79 1000
5 0.98 0.97 0.97 1000
6 0.67 0.79 0.73 1000
7 0.93 0.98 0.96 1000
8 0.97 0.97 0.97 1000
9 0.98 0.94 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[840]
precision recall f1-score support
0 0.85 0.83 0.84 1000
1 0.99 0.98 0.99 1000
2 0.83 0.78 0.80 1000
3 0.92 0.89 0.90 1000
4 0.78 0.87 0.82 1000
5 0.98 0.97 0.98 1000
6 0.71 0.71 0.71 1000
7 0.94 0.97 0.96 1000
8 0.97 0.97 0.97 1000
9 0.97 0.95 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[850]
precision recall f1-score support
0 0.86 0.83 0.84 1000
1 0.99 0.98 0.99 1000
2 0.85 0.76 0.80 1000
3 0.91 0.91 0.91 1000
4 0.75 0.89 0.82 1000
5 0.97 0.97 0.97 1000
6 0.75 0.71 0.73 1000
7 0.95 0.96 0.96 1000
8 0.97 0.97 0.97 1000
9 0.96 0.96 0.96 1000
accuracy 0.90 10000
macro avg 0.90 0.90 0.90 10000
weighted avg 0.90 0.90 0.90 10000
[860]
precision recall f1-score support
0 0.80 0.86 0.83 1000
1 0.99 0.98 0.98 1000
2 0.81 0.83 0.82 1000
3 0.91 0.90 0.90 1000
4 0.83 0.82 0.83 1000
5 0.98 0.97 0.98 1000
6 0.74 0.69 0.72 1000
7 0.95 0.97 0.96 1000
8 0.98 0.97 0.98 1000
9 0.97 0.97 0.97 1000
accuracy 0.90 10000
macro avg 0.90 0.90 0.90 10000
weighted avg 0.90 0.90 0.90 10000
[870]
precision recall f1-score support
0 0.85 0.85 0.85 1000
1 0.99 0.98 0.99 1000
2 0.75 0.86 0.80 1000
3 0.92 0.89 0.91 1000
4 0.83 0.83 0.83 1000
5 0.98 0.97 0.97 1000
6 0.78 0.69 0.73 1000
7 0.96 0.96 0.96 1000
8 0.98 0.97 0.98 1000
9 0.96 0.97 0.96 1000
accuracy 0.90 10000
macro avg 0.90 0.90 0.90 10000
weighted avg 0.90 0.90 0.90 10000
[880]
precision recall f1-score support
0 0.88 0.76 0.82 1000
1 0.99 0.98 0.99 1000
2 0.73 0.86 0.79 1000
3 0.89 0.92 0.90 1000
4 0.86 0.73 0.79 1000
5 0.98 0.95 0.97 1000
6 0.69 0.75 0.72 1000
7 0.95 0.97 0.96 1000
8 0.98 0.97 0.97 1000
9 0.95 0.97 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[890]
precision recall f1-score support
0 0.80 0.90 0.85 1000
1 0.98 0.98 0.98 1000
2 0.83 0.81 0.82 1000
3 0.92 0.88 0.90 1000
4 0.79 0.86 0.83 1000
5 0.98 0.97 0.98 1000
6 0.78 0.66 0.71 1000
7 0.96 0.96 0.96 1000
8 0.98 0.98 0.98 1000
9 0.96 0.98 0.97 1000
accuracy 0.90 10000
macro avg 0.90 0.90 0.90 10000
weighted avg 0.90 0.90 0.90 10000
[900]
precision recall f1-score support
0 0.79 0.89 0.84 1000
1 0.98 0.99 0.99 1000
2 0.79 0.85 0.81 1000
3 0.91 0.90 0.90 1000
4 0.85 0.80 0.82 1000
5 0.98 0.97 0.98 1000
6 0.79 0.66 0.72 1000
7 0.96 0.97 0.96 1000
8 0.97 0.98 0.98 1000
9 0.97 0.97 0.97 1000
accuracy 0.90 10000
macro avg 0.90 0.90 0.90 10000
weighted avg 0.90 0.90 0.90 10000
[910]
precision recall f1-score support
0 0.90 0.74 0.81 1000
1 0.99 0.98 0.99 1000
2 0.81 0.82 0.81 1000
3 0.88 0.90 0.89 1000
4 0.78 0.87 0.82 1000
5 0.99 0.96 0.97 1000
6 0.70 0.70 0.70 1000
7 0.94 0.97 0.96 1000
8 0.96 0.98 0.97 1000
9 0.96 0.96 0.96 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[920]
precision recall f1-score support
0 0.82 0.84 0.83 1000
1 0.98 0.99 0.98 1000
2 0.80 0.82 0.81 1000
3 0.92 0.89 0.91 1000
4 0.84 0.80 0.82 1000
5 0.98 0.97 0.98 1000
6 0.71 0.72 0.71 1000
7 0.93 0.98 0.95 1000
8 0.97 0.98 0.98 1000
9 0.97 0.94 0.95 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[930]
precision recall f1-score support
0 0.83 0.86 0.84 1000
1 0.99 0.98 0.98 1000
2 0.74 0.89 0.81 1000
3 0.91 0.89 0.90 1000
4 0.86 0.75 0.80 1000
5 0.98 0.97 0.98 1000
6 0.74 0.70 0.72 1000
7 0.96 0.97 0.96 1000
8 0.98 0.97 0.97 1000
9 0.96 0.96 0.96 1000
accuracy 0.89 10000
macro avg 0.90 0.89 0.89 10000
weighted avg 0.90 0.89 0.89 10000
[940]
precision recall f1-score support
0 0.86 0.80 0.83 1000
1 0.98 0.99 0.98 1000
2 0.84 0.76 0.80 1000
3 0.95 0.82 0.88 1000
4 0.74 0.87 0.80 1000
5 0.98 0.97 0.97 1000
6 0.68 0.77 0.73 1000
7 0.95 0.98 0.96 1000
8 0.98 0.97 0.97 1000
9 0.97 0.96 0.97 1000
accuracy 0.89 10000
macro avg 0.89 0.89 0.89 10000
weighted avg 0.89 0.89 0.89 10000
[950]
precision recall f1-score support
0 0.84 0.87 0.85 1000
1 0.99 0.98 0.99 1000
2 0.83 0.82 0.82 1000
3 0.89 0.93 0.91 1000
4 0.79 0.88 0.83 1000
5 0.97 0.97 0.97 1000
6 0.79 0.65 0.71 1000
7 0.95 0.97 0.96 1000
8 0.97 0.98 0.97 1000
9 0.96 0.96 0.96 1000
accuracy 0.90 10000
macro avg 0.90 0.90 0.90 10000
weighted avg 0.90 0.90 0.90 10000
[960]
precision recall f1-score support
0 0.81 0.88 0.84 1000
1 0.99 0.98 0.99 1000
2 0.82 0.81 0.81 1000
3 0.93 0.87 0.90 1000
4 0.81 0.83 0.82 1000
5 0.98 0.98 0.98 1000
6 0.73 0.71 0.72 1000
7 0.95 0.97 0.96 1000
8 0.98 0.96 0.97 1000
9 0.97 0.96 0.96 1000
accuracy 0.89 10000
macro avg 0.90 0.89 0.89 10000
weighted avg 0.90 0.89 0.89 10000
[970]
precision recall f1-score support
0 0.83 0.86 0.84 1000
1 0.99 0.98 0.98 1000
2 0.83 0.81 0.82 1000
3 0.93 0.87 0.90 1000
4 0.82 0.79 0.81 1000
5 0.98 0.97 0.97 1000
6 0.69 0.75 0.72 1000
7 0.93 0.98 0.96 1000
8 0.98 0.97 0.97 1000
9 0.98 0.94 0.96 1000
accuracy 0.89 10000
macro avg 0.90 0.89 0.89 10000
weighted avg 0.90 0.89 0.89 10000
[980]
precision recall f1-score support
0 0.77 0.91 0.84 1000
1 0.99 0.98 0.99 1000
2 0.86 0.77 0.82 1000
3 0.88 0.94 0.91 1000
4 0.77 0.87 0.82 1000
5 0.98 0.96 0.97 1000
6 0.81 0.61 0.70 1000
7 0.95 0.95 0.95 1000
8 0.99 0.96 0.97 1000
9 0.95 0.97 0.96 1000
accuracy 0.89 10000
macro avg 0.90 0.89 0.89 10000
weighted avg 0.90 0.89 0.89 10000
[990]
precision recall f1-score support
0 0.83 0.86 0.85 1000
1 0.99 0.99 0.99 1000
2 0.82 0.81 0.82 1000
3 0.93 0.87 0.90 1000
4 0.79 0.87 0.83 1000
5 0.98 0.97 0.97 1000
6 0.74 0.70 0.72 1000
7 0.95 0.97 0.96 1000
8 0.98 0.97 0.98 1000
9 0.96 0.96 0.96 1000
accuracy 0.90 10000
macro avg 0.90 0.90 0.90 10000
weighted avg 0.90 0.90 0.90 10000
Best model: [550] Stable model: [770]
models = np.reshape(np.arange(10, 50, 1), (-1, 1))
build_and_evaluate_models_threading(models)
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]
[32]
[33]
[34]
[35]
[36]
[37]
[38]
[39]
[40]
[41]
[42]
[43]
[44]
[45]
[46]
[47]
[48]
[49]
precision recall f1-score support
0 0.76 0.86 0.81 1000
1 0.98 0.95 0.97 1000
2 0.79 0.67 0.72 1000
3 0.87 0.83 0.85 1000
4 0.72 0.82 0.77 1000
5 0.96 0.93 0.95 1000
6 0.65 0.62 0.63 1000
7 0.93 0.93 0.93 1000
8 0.95 0.94 0.94 1000
9 0.92 0.96 0.94 1000
accuracy 0.85 10000
macro avg 0.85 0.85 0.85 10000
weighted avg 0.85 0.85 0.85 10000
precision recall f1-score support
0 0.74 0.87 0.80 1000
1 0.98 0.96 0.97 1000
2 0.78 0.71 0.74 1000
3 0.88 0.81 0.84 1000
4 0.71 0.81 0.76 1000
5 0.95 0.93 0.94 1000
6 0.68 0.59 0.63 1000
7 0.93 0.92 0.92 1000
8 0.96 0.94 0.95 1000
9 0.93 0.96 0.94 1000
accuracy 0.85 10000
macro avg 0.85 0.85 0.85 10000
weighted avg 0.85 0.85 0.85 10000
precision recall f1-score support
0 0.84 0.76 0.80 1000
1 0.96 0.97 0.96 1000
2 0.74 0.74 0.74 1000
3 0.88 0.81 0.84 1000
4 0.76 0.76 0.76 1000
5 0.91 0.96 0.93 1000
6 0.59 0.68 0.63 1000
7 0.94 0.93 0.93 1000
8 0.96 0.93 0.94 1000
9 0.96 0.94 0.95 1000
accuracy 0.85 10000
macro avg 0.85 0.85 0.85 10000
weighted avg 0.85 0.85 0.85 10000
precision recall f1-score support
0 0.81 0.81 0.81 1000
1 0.97 0.96 0.97 1000
2 0.68 0.85 0.75 1000
3 0.85 0.85 0.85 1000
4 0.76 0.75 0.75 1000
5 0.95 0.94 0.95 1000
6 0.71 0.52 0.60 1000
7 0.92 0.94 0.93 1000
8 0.94 0.96 0.95 1000
9 0.95 0.95 0.95 1000
accuracy 0.85 10000
macro avg 0.85 0.85 0.85 10000
weighted avg 0.85 0.85 0.85 10000
precision recall f1-score support
0 0.80 0.82 0.81 1000
1 0.97 0.97 0.97 1000
2 0.70 0.82 0.76 1000
3 0.90 0.83 0.87 1000
4 0.76 0.76 0.76 1000
5 0.93 0.95 0.94 1000
6 0.70 0.58 0.64 1000
7 0.93 0.93 0.93 1000
8 0.92 0.95 0.94 1000
9 0.95 0.94 0.94 1000
accuracy 0.86 10000
macro avg 0.86 0.86 0.86 10000
weighted avg 0.86 0.86 0.86 10000
precision recall f1-score support
0 0.83 0.79 0.81 1000
1 0.98 0.96 0.97 1000
2 0.75 0.81 0.78 1000
3 0.79 0.90 0.84 1000
4 0.78 0.76 0.77 1000
5 0.95 0.93 0.94 1000
6 0.71 0.61 0.65 1000
7 0.91 0.96 0.93 1000
8 0.93 0.95 0.94 1000
9 0.95 0.93 0.94 1000
accuracy 0.86 10000
macro avg 0.86 0.86 0.86 10000
weighted avg 0.86 0.86 0.86 10000
precision recall f1-score support
0 0.83 0.80 0.82 1000
1 0.97 0.97 0.97 1000
2 0.74 0.80 0.77 1000
3 0.84 0.89 0.87 1000
4 0.77 0.81 0.79 1000
5 0.96 0.94 0.95 1000
6 0.74 0.61 0.67 1000
7 0.91 0.96 0.94 1000
8 0.95 0.95 0.95 1000
9 0.96 0.93 0.95 1000
accuracy 0.87 10000
macro avg 0.87 0.87 0.87 10000
weighted avg 0.87 0.87 0.87 10000
precision recall f1-score support
0 0.83 0.80 0.81 1000
1 0.97 0.96 0.97 1000
2 0.76 0.79 0.78 1000
3 0.77 0.92 0.84 1000
4 0.79 0.76 0.77 1000
5 0.97 0.92 0.94 1000
6 0.72 0.61 0.66 1000
7 0.90 0.97 0.93 1000
8 0.96 0.95 0.96 1000
9 0.96 0.94 0.95 1000
accuracy 0.86 10000
macro avg 0.86 0.86 0.86 10000
weighted avg 0.86 0.86 0.86 10000
precision recall f1-score support
0 0.78 0.85 0.81 1000
1 0.97 0.96 0.97 1000
2 0.72 0.81 0.76 1000
3 0.87 0.85 0.86 1000
4 0.79 0.73 0.76 1000
5 0.95 0.96 0.95 1000
6 0.67 0.61 0.64 1000
7 0.92 0.96 0.94 1000
8 0.97 0.94 0.95 1000
9 0.96 0.92 0.94 1000
accuracy 0.86 10000
macro avg 0.86 0.86 0.86 10000
weighted avg 0.86 0.86 0.86 10000
precision recall f1-score support
0 0.87 0.71 0.78 1000
1 0.98 0.97 0.97 1000
2 0.75 0.79 0.77 1000
3 0.79 0.89 0.84 1000
4 0.79 0.75 0.77 1000
5 0.93 0.95 0.94 1000
6 0.66 0.66 0.66 1000
7 0.95 0.90 0.92 1000
8 0.92 0.97 0.94 1000
9 0.92 0.95 0.94 1000
accuracy 0.85 10000
macro avg 0.86 0.85 0.85 10000
weighted avg 0.86 0.85 0.85 10000
precision recall f1-score support
0 0.81 0.82 0.82 1000
1 0.97 0.97 0.97 1000
2 0.74 0.81 0.77 1000
3 0.85 0.89 0.87 1000
4 0.79 0.78 0.78 1000
5 0.93 0.95 0.94 1000
6 0.72 0.60 0.66 1000
7 0.93 0.94 0.93 1000
8 0.93 0.95 0.94 1000
9 0.96 0.94 0.95 1000
accuracy 0.86 10000
macro avg 0.86 0.86 0.86 10000
weighted avg 0.86 0.86 0.86 10000
precision recall f1-score support
0 0.84 0.78 0.81 1000
1 0.96 0.98 0.97 1000
2 0.70 0.84 0.76 1000
3 0.92 0.82 0.87 1000
4 0.83 0.66 0.73 1000
5 0.97 0.93 0.95 1000
6 0.62 0.71 0.66 1000
7 0.91 0.96 0.93 1000
8 0.95 0.95 0.95 1000
9 0.95 0.93 0.94 1000
accuracy 0.86 10000
macro avg 0.86 0.86 0.86 10000
weighted avg 0.86 0.86 0.86 10000
precision recall f1-score support
0 0.72 0.91 0.80 1000
1 0.98 0.96 0.97 1000
2 0.81 0.69 0.75 1000
3 0.90 0.83 0.87 1000
4 0.70 0.89 0.78 1000
5 0.97 0.94 0.95 1000
6 0.75 0.53 0.62 1000
7 0.93 0.93 0.93 1000
8 0.96 0.96 0.96 1000
9 0.93 0.96 0.95 1000
accuracy 0.86 10000
macro avg 0.87 0.86 0.86 10000
weighted avg 0.87 0.86 0.86 10000
precision recall f1-score support
0 0.80 0.83 0.82 1000
1 0.98 0.96 0.97 1000
2 0.78 0.78 0.78 1000
3 0.87 0.86 0.87 1000
4 0.79 0.79 0.79 1000
5 0.95 0.95 0.95 1000
6 0.68 0.68 0.68 1000
7 0.94 0.94 0.94 1000
8 0.97 0.94 0.96 1000
9 0.94 0.95 0.95 1000
accuracy 0.87 10000
macro avg 0.87 0.87 0.87 10000
weighted avg 0.87 0.87 0.87 10000
precision recall f1-score support
0 0.84 0.77 0.81 1000
1 0.99 0.96 0.98 1000
2 0.76 0.80 0.78 1000
3 0.85 0.89 0.87 1000
4 0.79 0.78 0.79 1000
5 0.96 0.94 0.95 1000
6 0.68 0.68 0.68 1000
7 0.91 0.97 0.94 1000
8 0.93 0.96 0.94 1000
9 0.96 0.92 0.94 1000
accuracy 0.87 10000
macro avg 0.87 0.87 0.87 10000
weighted avg 0.87 0.87 0.87 10000
precision recall f1-score support
0 0.81 0.85 0.83 1000
1 0.98 0.97 0.97 1000
2 0.80 0.76 0.78 1000
3 0.89 0.86 0.87 1000
4 0.75 0.84 0.79 1000
5 0.93 0.97 0.95 1000
6 0.71 0.64 0.67 1000
7 0.95 0.91 0.93 1000
8 0.95 0.95 0.95 1000
9 0.94 0.95 0.95 1000
accuracy 0.87 10000
macro avg 0.87 0.87 0.87 10000
weighted avg 0.87 0.87 0.87 10000
precision recall f1-score support
0 0.81 0.82 0.82 1000
1 0.97 0.98 0.97 1000
2 0.77 0.78 0.77 1000
3 0.86 0.87 0.87 1000
4 0.72 0.86 0.79 1000
5 0.97 0.93 0.95 1000
6 0.71 0.57 0.64 1000
7 0.92 0.95 0.94 1000
8 0.98 0.93 0.95 1000
9 0.94 0.95 0.94 1000
accuracy 0.86 10000
macro avg 0.86 0.86 0.86 10000
weighted avg 0.86 0.86 0.86 10000
precision recall f1-score support
0 0.80 0.84 0.82 1000
1 0.97 0.96 0.97 1000
2 0.83 0.66 0.73 1000
3 0.85 0.89 0.87 1000
4 0.73 0.84 0.78 1000
5 0.96 0.95 0.95 1000
6 0.67 0.66 0.67 1000
7 0.91 0.96 0.93 1000
8 0.98 0.93 0.96 1000
9 0.96 0.91 0.94 1000
accuracy 0.86 10000
macro avg 0.86 0.86 0.86 10000
weighted avg 0.86 0.86 0.86 10000
precision recall f1-score support
0 0.86 0.77 0.81 1000
1 0.97 0.98 0.97 1000
2 0.76 0.80 0.78 1000
3 0.81 0.90 0.85 1000
4 0.76 0.84 0.80 1000
5 0.95 0.95 0.95 1000
6 0.75 0.62 0.68 1000
7 0.93 0.94 0.94 1000
8 0.95 0.94 0.95 1000
9 0.95 0.94 0.95 1000
accuracy 0.87 10000
macro avg 0.87 0.87 0.87 10000
weighted avg 0.87 0.87 0.87 10000
precision recall f1-score support
0 0.76 0.88 0.81 1000
1 0.98 0.97 0.97 1000
2 0.71 0.86 0.78 1000
3 0.88 0.88 0.88 1000
4 0.81 0.74 0.77 1000
5 0.97 0.94 0.95 1000
6 0.76 0.57 0.65 1000
7 0.94 0.93 0.94 1000
8 0.96 0.94 0.95 1000
9 0.92 0.97 0.94 1000
accuracy 0.87 10000
macro avg 0.87 0.87 0.87 10000
weighted avg 0.87 0.87 0.87 10000
precision recall f1-score support
0 0.82 0.81 0.82 1000
1 0.97 0.97 0.97 1000
2 0.78 0.80 0.79 1000
3 0.88 0.88 0.88 1000
4 0.79 0.80 0.80 1000
5 0.95 0.96 0.95 1000
6 0.69 0.69 0.69 1000
7 0.95 0.93 0.94 1000
8 0.97 0.94 0.95 1000
9 0.94 0.96 0.95 1000
accuracy 0.87 10000
macro avg 0.87 0.87 0.87 10000
weighted avg 0.87 0.87 0.87 10000
precision recall f1-score support
0 0.81 0.85 0.83 1000
1 0.99 0.96 0.98 1000
2 0.79 0.74 0.77 1000
3 0.89 0.86 0.87 1000
4 0.72 0.87 0.79 1000
5 0.97 0.94 0.95 1000
6 0.71 0.63 0.67 1000
7 0.93 0.95 0.94 1000
8 0.97 0.94 0.95 1000
9 0.94 0.95 0.94 1000
accuracy 0.87 10000
macro avg 0.87 0.87 0.87 10000
weighted avg 0.87 0.87 0.87 10000
precision recall f1-score support
0 0.77 0.89 0.82 1000
1 0.97 0.97 0.97 1000
2 0.81 0.74 0.77 1000
3 0.88 0.87 0.87 1000
4 0.73 0.85 0.79 1000
5 0.96 0.96 0.96 1000
6 0.75 0.60 0.67 1000
7 0.95 0.94 0.94 1000
8 0.97 0.95 0.96 1000
9 0.95 0.96 0.95 1000
accuracy 0.87 10000
macro avg 0.87 0.87 0.87 10000
weighted avg 0.87 0.87 0.87 10000
precision recall f1-score support
0 0.82 0.82 0.82 1000
1 0.98 0.97 0.98 1000
2 0.69 0.87 0.77 1000
3 0.88 0.86 0.87 1000
4 0.84 0.66 0.73 1000
5 0.96 0.96 0.96 1000
6 0.68 0.67 0.68 1000
7 0.92 0.96 0.94 1000
8 0.97 0.95 0.96 1000
9 0.97 0.94 0.95 1000
accuracy 0.87 10000
macro avg 0.87 0.87 0.87 10000
weighted avg 0.87 0.87 0.87 10000
precision recall f1-score support
0 0.89 0.65 0.75 1000
1 0.98 0.97 0.98 1000
2 0.85 0.70 0.77 1000
3 0.88 0.85 0.86 1000
4 0.82 0.71 0.76 1000
5 0.97 0.95 0.96 1000
6 0.51 0.84 0.64 1000
7 0.93 0.97 0.95 1000
8 0.98 0.94 0.96 1000
9 0.96 0.95 0.95 1000
accuracy 0.85 10000
macro avg 0.88 0.85 0.86 10000
weighted avg 0.88 0.85 0.86 10000
precision recall f1-score support
0 0.82 0.81 0.82 1000
1 0.97 0.97 0.97 1000
2 0.80 0.75 0.77 1000
3 0.90 0.84 0.87 1000
4 0.81 0.76 0.78 1000
5 0.96 0.95 0.95 1000
6 0.64 0.74 0.69 1000
7 0.93 0.96 0.94 1000
8 0.96 0.96 0.96 1000
9 0.96 0.94 0.95 1000
accuracy 0.87 10000
macro avg 0.87 0.87 0.87 10000
weighted avg 0.87 0.87 0.87 10000
precision recall f1-score support
0 0.83 0.78 0.81 1000
1 0.97 0.98 0.97 1000
2 0.80 0.75 0.77 1000
3 0.91 0.87 0.89 1000
4 0.75 0.85 0.80 1000
5 0.95 0.95 0.95 1000
6 0.68 0.69 0.69 1000
7 0.94 0.94 0.94 1000
8 0.95 0.96 0.95 1000
9 0.95 0.95 0.95 1000
accuracy 0.87 10000
macro avg 0.87 0.87 0.87 10000
weighted avg 0.87 0.87 0.87 10000
precision recall f1-score support
0 0.78 0.83 0.80 1000
1 0.98 0.97 0.97 1000
2 0.79 0.80 0.79 1000
3 0.78 0.94 0.86 1000
4 0.81 0.81 0.81 1000
5 0.98 0.94 0.96 1000
6 0.76 0.59 0.66 1000
7 0.91 0.97 0.94 1000
8 0.98 0.93 0.95 1000
9 0.96 0.94 0.95 1000
accuracy 0.87 10000
macro avg 0.87 0.87 0.87 10000
weighted avg 0.87 0.87 0.87 10000
precision recall f1-score support
0 0.85 0.74 0.79 1000
1 0.98 0.97 0.97 1000
2 0.84 0.71 0.77 1000
3 0.87 0.89 0.88 1000
4 0.81 0.76 0.79 1000
5 0.97 0.93 0.95 1000
6 0.58 0.78 0.67 1000
7 0.91 0.96 0.93 1000
8 0.96 0.96 0.96 1000
9 0.95 0.93 0.94 1000
accuracy 0.86 10000
macro avg 0.87 0.86 0.86 10000
weighted avg 0.87 0.86 0.86 10000
precision recall f1-score support
0 0.83 0.77 0.80 1000
1 0.98 0.97 0.97 1000
2 0.87 0.62 0.73 1000
3 0.88 0.88 0.88 1000
4 0.74 0.82 0.78 1000
5 0.95 0.97 0.96 1000
6 0.61 0.78 0.68 1000
7 0.95 0.93 0.94 1000
8 0.97 0.94 0.95 1000
9 0.94 0.96 0.95 1000
accuracy 0.86 10000
macro avg 0.87 0.86 0.86 10000
weighted avg 0.87 0.86 0.86 10000
precision recall f1-score support
0 0.83 0.82 0.82 1000
1 0.99 0.97 0.98 1000
2 0.79 0.81 0.80 1000
3 0.87 0.89 0.88 1000
4 0.79 0.80 0.80 1000
5 0.95 0.95 0.95 1000
6 0.70 0.68 0.69 1000
7 0.91 0.96 0.94 1000
8 0.96 0.95 0.95 1000
9 0.97 0.93 0.95 1000
accuracy 0.88 10000
macro avg 0.88 0.88 0.88 10000
weighted avg 0.88 0.88 0.88 10000
precision recall f1-score support
0 0.78 0.86 0.82 1000
1 0.97 0.97 0.97 1000
2 0.76 0.82 0.79 1000
3 0.88 0.87 0.88 1000
4 0.80 0.79 0.79 1000
5 0.95 0.96 0.96 1000
6 0.75 0.63 0.68 1000
7 0.93 0.95 0.94 1000
8 0.96 0.95 0.96 1000
9 0.95 0.94 0.94 1000
accuracy 0.87 10000
macro avg 0.87 0.87 0.87 10000
weighted avg 0.87 0.87 0.87 10000
precision recall f1-score support
0 0.83 0.83 0.83 1000
1 0.99 0.97 0.98 1000
2 0.68 0.88 0.77 1000
3 0.86 0.90 0.88 1000
4 0.87 0.67 0.76 1000
5 0.96 0.96 0.96 1000
6 0.71 0.66 0.68 1000
7 0.93 0.96 0.95 1000
8 0.97 0.94 0.96 1000
9 0.96 0.94 0.95 1000
accuracy 0.87 10000
macro avg 0.88 0.87 0.87 10000
weighted avg 0.88 0.87 0.87 10000
precision recall f1-score support
0 0.76 0.88 0.82 1000
1 0.99 0.95 0.97 1000
2 0.80 0.79 0.79 1000
3 0.84 0.91 0.87 1000
4 0.81 0.78 0.79 1000
5 0.97 0.93 0.95 1000
6 0.74 0.62 0.68 1000
7 0.94 0.95 0.94 1000
8 0.95 0.95 0.95 1000
9 0.94 0.95 0.94 1000
accuracy 0.87 10000
macro avg 0.87 0.87 0.87 10000
weighted avg 0.87 0.87 0.87 10000
precision recall f1-score support
0 0.71 0.91 0.80 1000
1 0.99 0.97 0.98 1000
2 0.80 0.77 0.78 1000
3 0.89 0.88 0.88 1000
4 0.84 0.74 0.79 1000
5 0.97 0.95 0.96 1000
6 0.70 0.65 0.67 1000
7 0.93 0.96 0.94 1000
8 0.97 0.94 0.95 1000
9 0.95 0.95 0.95 1000
accuracy 0.87 10000
macro avg 0.87 0.87 0.87 10000
weighted avg 0.87 0.87 0.87 10000
precision recall f1-score support
0 0.85 0.80 0.82 1000
1 0.97 0.97 0.97 1000
2 0.80 0.79 0.79 1000
3 0.86 0.91 0.88 1000
4 0.81 0.81 0.81 1000
5 0.96 0.95 0.96 1000
6 0.70 0.71 0.70 1000
7 0.93 0.96 0.94 1000
8 0.96 0.95 0.96 1000
9 0.96 0.95 0.95 1000
accuracy 0.88 10000
macro avg 0.88 0.88 0.88 10000
weighted avg 0.88 0.88 0.88 10000
precision recall f1-score support
0 0.80 0.87 0.83 1000
1 0.97 0.97 0.97 1000
2 0.75 0.81 0.78 1000
3 0.87 0.87 0.87 1000
4 0.77 0.82 0.79 1000
5 0.97 0.95 0.96 1000
6 0.78 0.59 0.67 1000
7 0.94 0.95 0.94 1000
8 0.96 0.95 0.96 1000
9 0.94 0.96 0.95 1000
accuracy 0.87 10000
macro avg 0.87 0.87 0.87 10000
weighted avg 0.87 0.87 0.87 10000
precision recall f1-score support
0 0.82 0.83 0.82 1000
1 0.96 0.97 0.97 1000
2 0.76 0.82 0.79 1000
3 0.87 0.89 0.88 1000
4 0.80 0.82 0.81 1000
5 0.96 0.95 0.96 1000
6 0.76 0.63 0.69 1000
7 0.94 0.94 0.94 1000
8 0.96 0.96 0.96 1000
9 0.94 0.95 0.94 1000
accuracy 0.88 10000
macro avg 0.88 0.88 0.88 10000
weighted avg 0.88 0.88 0.88 10000
precision recall f1-score support
0 0.90 0.68 0.77 1000
1 0.98 0.97 0.98 1000
2 0.68 0.87 0.76 1000
3 0.84 0.89 0.87 1000
4 0.78 0.80 0.79 1000
5 0.96 0.96 0.96 1000
6 0.69 0.63 0.66 1000
7 0.92 0.96 0.94 1000
8 0.96 0.95 0.96 1000
9 0.96 0.93 0.95 1000
accuracy 0.86 10000
macro avg 0.87 0.86 0.86 10000
weighted avg 0.87 0.86 0.86 10000
precision recall f1-score support
0 0.84 0.81 0.82 1000
1 0.97 0.97 0.97 1000
2 0.84 0.69 0.76 1000
3 0.89 0.86 0.87 1000
4 0.72 0.86 0.78 1000
5 0.96 0.97 0.96 1000
6 0.67 0.71 0.69 1000
7 0.96 0.91 0.93 1000
8 0.98 0.95 0.97 1000
9 0.92 0.96 0.94 1000
accuracy 0.87 10000
macro avg 0.87 0.87 0.87 10000
weighted avg 0.87 0.87 0.87 10000
(27, 35)
models = np.reshape(np.arange(10, 50, 1), (-1, 1))
best_model, stable_model = build_and_evaluate_models([[], [10], [20]])
print('Best model: ', models[best_model])
print('Stable model: ', models[stable_model])
[]
<keras.engine.sequential.Sequential object at 0x000001C35CDC04F0>
precision recall f1-score support
0 0.77 0.84 0.81 1000
1 0.96 0.96 0.96 1000
2 0.71 0.75 0.73 1000
3 0.87 0.84 0.85 1000
4 0.75 0.71 0.73 1000
5 0.94 0.91 0.93 1000
6 0.61 0.56 0.58 1000
7 0.91 0.93 0.92 1000
8 0.93 0.95 0.94 1000
9 0.94 0.95 0.94 1000
accuracy 0.84 10000
macro avg 0.84 0.84 0.84 10000
weighted avg 0.84 0.84 0.84 10000
[10]
<keras.engine.sequential.Sequential object at 0x000001C355365F60>
precision recall f1-score support
0 0.76 0.87 0.81 1000
1 0.97 0.95 0.96 1000
2 0.80 0.64 0.71 1000
3 0.86 0.84 0.85 1000
4 0.68 0.83 0.75 1000
5 0.96 0.91 0.93 1000
6 0.64 0.55 0.59 1000
7 0.91 0.93 0.92 1000
8 0.94 0.94 0.94 1000
9 0.92 0.95 0.94 1000
accuracy 0.84 10000
macro avg 0.84 0.84 0.84 10000
weighted avg 0.84 0.84 0.84 10000
[20]
<keras.engine.sequential.Sequential object at 0x000001C35EE99F00>
precision recall f1-score support
0 0.86 0.68 0.76 1000
1 0.98 0.95 0.97 1000
2 0.81 0.64 0.71 1000
3 0.89 0.82 0.85 1000
4 0.77 0.61 0.69 1000
5 0.96 0.92 0.94 1000
6 0.46 0.80 0.58 1000
7 0.91 0.95 0.93 1000
8 0.95 0.94 0.95 1000
9 0.94 0.95 0.95 1000
accuracy 0.83 10000
macro avg 0.85 0.83 0.83 10000
weighted avg 0.85 0.83 0.83 10000
Best model: [12] Stable model: [10]
models = np.reshape(np.arange(10, 50, 1), (-1, 1))
build_and_evaluate_models_threading(models)
[10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48] [49]
It is interesting to see that where it got confused is between similar items. The shirt is mainly confused with T-shirt/top, pullover, coat, and dress. All of these are, for the most part, the same shape and reflects in the precision and recall values; they are the lowest across the board. On the other hand, the item with the best scores is the most unique out of the list, trousers.